Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM

Authors

Yefeng Jiao, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;Follow
Yan Wang, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;Follow
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: To effectively predict the short-term wind power and its fluctuation range, a prediction method based on hybrid algorithm-optimized support vector machine is proposed. Exploratory data analysis is used to preprocess the original wind speed data to improve the data quality. Chaotic map, Levy flight strategy and particle swarm optimization are used to improve the ant lion algorithm. The support vector machine model optimized by hybrid algorithm is used to predict the wind power. The experimental results show that, compared with the new wind power prediction model, the prediction error of the output results of the method is lower, and the wind power. prediction ability is better.

Recommended Citation

Jiao, Yefeng; Wang, Yan; and Ji, Zhicheng (2022) "Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM," Journal of System Simulation: Vol. 34: Iss. 12, Article 3.
DOI: 10.16182/j.issn1004731x.joss.22-FZ0924
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss12/3

First Page

2535

Revised Date

2022-10-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.22-FZ0924

Last Page

2545

CLC

TP391.9

Recommended Citation

Yefeng Jiao, Yan Wang, Zhicheng Ji. Short-term Prediction Method of Wind Power Based on BLP-ALO-SVM[J]. Journal of System Simulation, 2022, 34(12): 2535-2545.

Corresponding Author

Yan Wang,wangyan@jiangnan.edu.cn

DOI

10.16182/j.issn1004731x.joss.22-FZ0924